traceability between sdtm and adam converted analysis … presentations/cd06.pdf · sdtm conversion...
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Traceability between SDTM and ADaM converted analysis datasets
Topics
1 Introduction
2 ADaM Conversion
4 Challenges & Conclusion
3 Quality Control
SDTM/ADaM adoption by FDA
• SDTM is expected to be « required for FDA submission » within 2 years
– CDER is accepting SDTM submissions – CBER is accepting SDTM submissions since May 2010 – CDRH interest is rising, CDISC SDTM team has formed a medical
devices subteam • FDA CDER:
– Requesting sponsors to submit in SDTM format – Encouraging sponsors to submit in ADaM format
• Continuous FDA pilot projects, both CDER and CBER
Implementation approaches: strategy 1
SDTM CONVERSION METADATA CREATION
CONVERSIONSOURCE
CLINICAL DATABASE
ANALYSIS DATABASE
CRFs ANNOTATION SDTM
CLINICAL DATABASE
SDTM DEFINE.XML
CRFsANNOTATION
METADATA CREATIONANALYSIS DATABASE
ADaM DEFINE.XML
ANALYSIS DATASET PREPARATION
ANALYSIS DATASET PREPARATION
STATISTICALOUTPUTS
STATISTICAL OUTPUTS
ANALYSIS RESULTS PREPARATION
ANALYSIS RESULTS PREPARATION
COMPARISON
Implementation approaches: strategy 2
SDTM CONVERSION METADATA CREATION
CONVERSIONSOURCE
CLINICAL DATABASE
ANALYSIS DATABASE
CRFs ANNOTATION SDTM
CLINICAL DATABASE
SDTM DEFINE.XML
CRFsANNOTATION
METADATA CREATIONANALYSIS DATABASE
ADaM DEFINE.XML
ANALYSIS DATASET PREPARATION
STATISTICALOUTPUTS
STATISTICAL OUTPUTS
ANALYSIS RESULTS PREPARATION
ANALYSIS RESULTS PREPARATION
ADaM CONVERSION
TRACEABILITYTRACEABILITY
COMPARISON
Traceability SDTM and ADaM
• Understanding relationship between the analysis results, the analysis datasets and the SDTM domains
• Establishing the path between an element and its immediate
predecessor
• Two levels: – Metadata traceability
• Relationship between an analysis result and analysis dataset(s) • Relationship of the analysis variable to its source dataset(s) and
variable(s)
– Data point traceability • Predecessor record(s)
Traceability SDTM and ADaM
Analysis Results
Analysis Dataset
ADaM define.xml SDTM define.xml
SDTM aCRF
Topics
1 Introduction
2 ADaM Conversion
4 Challenges & Conclusion
3 Quality Control
DEFINE.XML
MAPPING SHEET
ADaM
STATISTICAL OUTPUTS
ADaM Conversion: strategy 2
STATISTICAL OUTPUTS
> STATISTICAL ANALYSIS PLAN> PROGRAMS> ANALYSIS SPECIFICATIONS
ANALYSISDATASETS
CLINICAL DATA
DEFINE.XML
MAPPING SHEET
SDTM
TRACEABILITY
COMPARISON
Number of studies and ADs
• Submission included 11 trials
• For each trial: – ADSL (Subject Level Analysis Dataset) – AD with baseline conditions – AD with treatment administration – AD with efficacy endpoints
• For some trials: – 2 Pharmacokinetic datasets
Team Profile and Roles
• CRO Manager – CDISC expert support
• Project Manager Project Manager back-up – Assigned for the duration of the project – Single point of contact
• Mappers (4) – ADaM experts – Define mapping – Investigate traceability
• Programmers (2.5) – Create the conversions programs – Perform peer review
• Data Steward (0.5) – Maintains the consistency across the project
• Quality Checker (4) – Perform ADaM datasets review – Perform define.xml review
Conversion Types
• Creation of SDTM variables – Variables like USUBJID which were created during the SDTM
convertion
• Minor conversion – Contents unchanged, metadata changes – Change variable name and label of the age group variable
• Format values – Content and metadata changes – The content of the SEX variable had to be changed in order to reflect the
SDTM values
• Transpose – Observations become variables – Populations in the ADSL dataset
Traceability
• Variables originating from SDTM – SDTM variables are retained in ADaM ADs for traceability – SDTM variables are unchanged
• same name, same type, same label (metadata) • and same content (data)
• Derived variables – Original computational algorithm for derived AD variable(s) based on
original clinical database – New computational algorithm needs to be based on SDTM database – New computational algorithm is included into ADaM define.xml
Topics
1 Introduction
2 ADaM Conversion
4 Challenges & Conclusion
3 Quality Control
Quality Control
• QC is partially automated – Electronic QC (CDISC Compliance Checks – SDTM&ADaM) – Manual QC – QC on Consistency (Data Steward)
• QC on:
– Mapping – ADaM Datasets – Define.xml – Statistical Results
• QC is supported by documentation
QC Tier 1: CDISC Compliance Checks
We have created an expanded & enhanced list of checks
• 154 WebSDM ™ checks • Total check package:
CDISC compliance checks list is growing continuously
SDTMIG V3.1.1
SDTMIG V3.1.2
ADaMIG V1.0
Data checks 141 219 45
Metadata checks 68 117 51
Mapping checks 56 57 12
Project consistency checks
20 20 20
SAS® DI STUDIO
DEFINE.XML
CHECKSELECTION
CHECK SCHEDULER
EXCEPTIONTABLE
COMPLIANCEISSUE REPORT
SDTMIG V3.1.1SDTMIG V3.1.2ADaMIG V1.0METADATA
LIBRARYMETADATADATABASE
SDTM DATASETS
ADaM DATASETS
QC Tier 1: Application Flowchart
QC Tier 2: Manual QC
• 100% manual QC on a random sample • Supported by checklists • Supported by a QC content tool on source and target
QC Tier 3: Data Steward
• Maintains consistency of metadata across project • Uses the metadata repository • Electronic consistency checks
TRANSFORMATION TRANSFORMATION
TRIAL ADSsTRIAL RESULTS POLLED ADSs ADaM ADaM RESULTS
COMPARISON
ADaM QC
TRIAL-1
TRIAL-2
TRIAL-3
TRIAL-n
QC Tier 4: Statistical Results
QC Tier 4: Team Profile and Roles
• Project-/Trial Programmer (3) – Coordination – Single point of contact
• Project Statistician (1) – Specifications of
results subject to QC
• QC Programmers (3) – Re-production of
statistical results
12 ADaM CONVERTERS BDLS 3 QC PROGRAMMERS
5 PROJECT/TRIAL PROGRAMMERS
1 PROJECT ASSISTANT
QC Tier 4 : Tasks
• Compilation of selected result-tables – ~ 55 table types – ~ 220 tables – mainly descriptive statistics – few inferential statistics (ANCOVA)
• Set-up of work environment – e.g. directories, access rights
• Learning the project, trials
• QC Programming – Recreate results from CTR / ISE – Based on Pooled BI Analysis Datasets (initially) – Based on ADaM (once available)
• Documenting QC progress
• Comparison of results
Communication Topics
• Report Source Data Issues – Empty variables – Exclusion of screen failures – Unclear computational algorithms – Traceability issues with SDTM
• Sponsor Feedback
– Clarifications computational algorithms – QC comments
Communication
• Addressing and solving issues and deciding further proceedings in
– weekly T*C with representatives from each of the 3 subteams
– daily brief QC Programmers meeting
• Communication was:
– Timely and immediate
– Focused
– For some last minute changes to ADaM, communication was not effective – e.g. renaming of variables – data changes due to B&D Life Sciences QC, e.g. indicator variables
Topics
1 Introduction
2 ADaM Conversion
4 Challenges & Conclusion
3 Quality Control
Challenges
• Learning the project / trials
• Understanding original analysis datasets and computational algorithms
• Finding all QC relevant result tables – Initially some wrong tables selected – Transformation from trial to pooled ADs not clearly documented
• This type of project is always on critical path for a submission – Short timelines – Large team
Conclusion
• We now understand better how FDA feels
• SDTM is the basis for analysis and therefore needs to be complete
• Results in the clinical study report must be reproducible by FDA reviewers from the newly created ADaM analysis datasets
• Traceability most difficult part in ADaM conversion
• Familiarization with usage of ADaM for programming was minimal – Due to similarity of ADaM with BI-ADs structure
• Relatively straightforward to program from ADaM
• In an ideal world, analysis datasets are created from SDTM datasets, thereby ensuring 100% traceability